Few Shot Image Classification On Meta Dataset
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Accuracy |
|---|---|
| improving-few-shot-visual-classification-with | 70.32 |
| prototypical-networks-for-few-shot-learning | 60.573 |
| matching-networks-for-one-shot-learning | 56.247 |
| improved-few-shot-visual-classification | 69.86 |
| task-specific-preconditioner-for-cross-domain | 81.40 |
| shallow-bayesian-meta-learning-for-real-world | 74.3 |
| meta-dataset-a-dataset-of-datasets-for | 63.428 |
| contextual-squeeze-and-excitation-for | 76.1 |
| improving-task-adaptation-for-cross-domain | 78.07 |
| meta-dataset-a-dataset-of-datasets-for | 54.319 |
| selecting-relevant-features-from-a-universal | 70.72 |
| selecting-relevant-features-from-a-universal | 69.3 |
| universal-representation-learning-from | 75.75 |
| exploring-complementary-strengths-of | 68.89 |
| meta-dataset-a-dataset-of-datasets-for | 58.758 |
| contextual-squeeze-and-excitation-for | 74.9 |
| model-agnostic-meta-learning-for-fast | 57.024 |
| a-universal-representation-transformer-layer | 72.15 |
| fast-and-flexible-multi-task-classification | 66.9 |
| pushing-the-limits-of-simple-pipelines-for | 84.75 |
| unleashing-the-power-of-meta-tuning-for-few | 85.27 |
| learning-to-compare-relation-network-for-few | 53.315 |